We describe a practical collision avoidance algorithm that synthesizes provably safe piecewise constant control laws (compatible with the sampled-data nature of the system), and demonstrate the results on an experimental platform, the Pioneer ground robots. Our application is formulated in a pursuer-evader framework in which an automated unmanned vehicle navigates its environment while avoiding a moving obstacle that acts as a malicious agent. Offline, we employ reachability analysis to characterize the evolution of trajectories so as to determine what control inputs can preserve safety over every sampling interval. The moving obstacle is considered unpredictable with nearly no restrictions on its control policies (although we do take into account the physical constraints due to limited dynamical and actuation capacities of both robots). Online, the controller executes computationally inexpensive operations based only on an easy-to-store lookup table. The results of the experiment as well as the proposed algorithm are presented and discussed in detail.